Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations52318
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 MiB
Average record size in memory120.0 B

Variable types

Numeric9
Categorical4
Boolean1

Alerts

age_of_first_emp is highly overall correlated with cb_person_cred_hist_length and 2 other fieldsHigh correlation
cb_person_cred_hist_length is highly overall correlated with age_of_first_emp and 1 other fieldsHigh correlation
cb_person_default_on_file is highly overall correlated with loan_grade and 1 other fieldsHigh correlation
loan_amnt is highly overall correlated with loan_percent_incomeHigh correlation
loan_grade is highly overall correlated with cb_person_default_on_file and 1 other fieldsHigh correlation
loan_int_rate is highly overall correlated with cb_person_default_on_file and 1 other fieldsHigh correlation
loan_percent_income is highly overall correlated with loan_amntHigh correlation
person_age is highly overall correlated with age_of_first_emp and 1 other fieldsHigh correlation
person_emp_length is highly overall correlated with age_of_first_empHigh correlation
id is uniformly distributed Uniform
id has unique values Unique
person_emp_length has 6749 (12.9%) zeros Zeros

Reproduction

Analysis started2025-03-11 17:37:31.180811
Analysis finished2025-03-11 17:37:42.576852
Duration11.4 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Uniform  Unique 

Distinct52318
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29328.758
Minimum0
Maximum58644
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size817.5 KiB
2025-03-11T13:37:42.662422image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2929.85
Q114629.25
median29314.5
Q344022.75
95-th percentile55744.15
Maximum58644
Range58644
Interquartile range (IQR)29393.5

Descriptive statistics

Standard deviation16958.414
Coefficient of variation (CV)0.57821793
Kurtosis-1.2024556
Mean29328.758
Median Absolute Deviation (MAD)14696
Skewness9.3031589 × 10-5
Sum1.534422 × 109
Variance2.875878 × 108
MonotonicityStrictly increasing
2025-03-11T13:37:42.782203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
39133 1
 
< 0.1%
39122 1
 
< 0.1%
39123 1
 
< 0.1%
39124 1
 
< 0.1%
39125 1
 
< 0.1%
39127 1
 
< 0.1%
39128 1
 
< 0.1%
39129 1
 
< 0.1%
39130 1
 
< 0.1%
Other values (52308) 52308
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
58644 1
< 0.1%
58643 1
< 0.1%
58642 1
< 0.1%
58641 1
< 0.1%
58639 1
< 0.1%
58638 1
< 0.1%
58637 1
< 0.1%
58636 1
< 0.1%
58635 1
< 0.1%
58634 1
< 0.1%

person_age
Real number (ℝ)

High correlation 

Distinct51
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.542051
Minimum20
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size817.5 KiB
2025-03-11T13:37:42.897625image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile22
Q123
median26
Q330
95-th percentile39
Maximum84
Range64
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.0092298
Coefficient of variation (CV)0.21818382
Kurtosis5.406357
Mean27.542051
Median Absolute Deviation (MAD)3
Skewness1.9107078
Sum1440945
Variance36.110843
MonotonicityNot monotonic
2025-03-11T13:37:43.012448image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 6914
13.2%
22 6282
12.0%
24 5698
10.9%
25 4503
 
8.6%
27 4024
 
7.7%
26 3451
 
6.6%
28 3304
 
6.3%
29 2920
 
5.6%
30 2095
 
4.0%
31 1705
 
3.3%
Other values (41) 11422
21.8%
ValueCountFrequency (%)
20 8
 
< 0.1%
21 1600
 
3.1%
22 6282
12.0%
23 6914
13.2%
24 5698
10.9%
25 4503
8.6%
26 3451
6.6%
27 4024
7.7%
28 3304
6.3%
29 2920
5.6%
ValueCountFrequency (%)
84 2
 
< 0.1%
80 2
 
< 0.1%
73 1
 
< 0.1%
70 10
< 0.1%
69 6
< 0.1%
66 9
< 0.1%
65 11
< 0.1%
64 9
< 0.1%
62 6
< 0.1%
61 13
< 0.1%

person_income
Real number (ℝ)

Distinct2376
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64076.099
Minimum9600
Maximum1900000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size817.5 KiB
2025-03-11T13:37:43.128690image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum9600
5-th percentile28800
Q142000
median58195
Q375531
95-th percentile120000
Maximum1900000
Range1890400
Interquartile range (IQR)33531

Descriptive statistics

Standard deviation35301.29
Coefficient of variation (CV)0.55092758
Kurtosis217.82996
Mean64076.099
Median Absolute Deviation (MAD)16805
Skewness7.3712001
Sum3.3523333 × 109
Variance1.2461811 × 109
MonotonicityNot monotonic
2025-03-11T13:37:43.251842image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 3904
 
7.5%
50000 2845
 
5.4%
30000 2059
 
3.9%
70000 1750
 
3.3%
75000 1583
 
3.0%
40000 1521
 
2.9%
45000 1489
 
2.8%
65000 1402
 
2.7%
90000 1268
 
2.4%
48000 1139
 
2.2%
Other values (2366) 33358
63.8%
ValueCountFrequency (%)
9600 8
 
< 0.1%
10140 1
 
< 0.1%
12000 26
< 0.1%
12360 1
 
< 0.1%
12500 1
 
< 0.1%
12600 1
 
< 0.1%
12996 1
 
< 0.1%
13200 6
 
< 0.1%
14000 3
 
< 0.1%
14400 44
0.1%
ValueCountFrequency (%)
1900000 1
 
< 0.1%
1200000 1
 
< 0.1%
900000 4
< 0.1%
780000 3
< 0.1%
762000 2
< 0.1%
741600 1
 
< 0.1%
700000 1
 
< 0.1%
636000 1
 
< 0.1%
600000 3
< 0.1%
564000 1
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size817.5 KiB
RENT
27274 
MORTGAGE
22191 
OWN
2772 
OTHER
 
81

Length

Max length8
Median length4
Mean length5.645189
Min length3

Characters and Unicode

Total characters295345
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowOWN
3rd rowOWN
4th rowRENT
5th rowRENT

Common Values

ValueCountFrequency (%)
RENT 27274
52.1%
MORTGAGE 22191
42.4%
OWN 2772
 
5.3%
OTHER 81
 
0.2%

Length

2025-03-11T13:37:43.375078image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T13:37:43.467524image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
rent 27274
52.1%
mortgage 22191
42.4%
own 2772
 
5.3%
other 81
 
0.2%

Most occurring characters

ValueCountFrequency (%)
R 49546
16.8%
E 49546
16.8%
T 49546
16.8%
G 44382
15.0%
N 30046
10.2%
O 25044
8.5%
M 22191
7.5%
A 22191
7.5%
W 2772
 
0.9%
H 81
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295345
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 49546
16.8%
E 49546
16.8%
T 49546
16.8%
G 44382
15.0%
N 30046
10.2%
O 25044
8.5%
M 22191
7.5%
A 22191
7.5%
W 2772
 
0.9%
H 81
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295345
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 49546
16.8%
E 49546
16.8%
T 49546
16.8%
G 44382
15.0%
N 30046
10.2%
O 25044
8.5%
M 22191
7.5%
A 22191
7.5%
W 2772
 
0.9%
H 81
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295345
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 49546
16.8%
E 49546
16.8%
T 49546
16.8%
G 44382
15.0%
N 30046
10.2%
O 25044
8.5%
M 22191
7.5%
A 22191
7.5%
W 2772
 
0.9%
H 81
 
< 0.1%

person_emp_length
Real number (ℝ)

High correlation  Zeros 

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6890363
Minimum0
Maximum39
Zeros6749
Zeros (%)12.9%
Negative0
Negative (%)0.0%
Memory size817.5 KiB
2025-03-11T13:37:43.565084image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile12
Maximum39
Range39
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.8749001
Coefficient of variation (CV)0.82637451
Kurtosis2.0111745
Mean4.6890363
Median Absolute Deviation (MAD)2
Skewness1.1703373
Sum245321
Variance15.01485
MonotonicityNot monotonic
2025-03-11T13:37:43.691320image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 6749
12.9%
2 6482
12.4%
3 5779
11.0%
5 5273
10.1%
4 4899
9.4%
1 4622
8.8%
6 4371
8.4%
7 3822
7.3%
8 2673
 
5.1%
9 2053
 
3.9%
Other values (23) 5595
10.7%
ValueCountFrequency (%)
0 6749
12.9%
1 4622
8.8%
2 6482
12.4%
3 5779
11.0%
4 4899
9.4%
5 5273
10.1%
6 4371
8.4%
7 3822
7.3%
8 2673
 
5.1%
9 2053
 
3.9%
ValueCountFrequency (%)
39 1
 
< 0.1%
31 4
 
< 0.1%
30 2
 
< 0.1%
29 4
 
< 0.1%
28 3
 
< 0.1%
27 5
 
< 0.1%
26 8
< 0.1%
25 7
< 0.1%
24 13
< 0.1%
23 10
< 0.1%

loan_intent
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size817.5 KiB
EDUCATION
11031 
MEDICAL
9521 
PERSONAL
9003 
VENTURE
8953 
DEBTCONSOLIDATION
8217 

Length

Max length17
Median length15
Mean length10.019592
Min length7

Characters and Unicode

Total characters524205
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEDUCATION
2nd rowMEDICAL
3rd rowPERSONAL
4th rowVENTURE
5th rowMEDICAL

Common Values

ValueCountFrequency (%)
EDUCATION 11031
21.1%
MEDICAL 9521
18.2%
PERSONAL 9003
17.2%
VENTURE 8953
17.1%
DEBTCONSOLIDATION 8217
15.7%
HOMEIMPROVEMENT 5593
10.7%

Length

2025-03-11T13:37:43.933909image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T13:37:44.032467image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
education 11031
21.1%
medical 9521
18.2%
personal 9003
17.2%
venture 8953
17.1%
debtconsolidation 8217
15.7%
homeimprovement 5593
10.7%

Most occurring characters

ValueCountFrequency (%)
E 72457
13.8%
O 55871
10.7%
N 51014
9.7%
I 42579
8.1%
T 42011
8.0%
A 37772
 
7.2%
D 36986
 
7.1%
C 28769
 
5.5%
L 26741
 
5.1%
M 26300
 
5.0%
Other values (7) 103705
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 524205
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 72457
13.8%
O 55871
10.7%
N 51014
9.7%
I 42579
8.1%
T 42011
8.0%
A 37772
 
7.2%
D 36986
 
7.1%
C 28769
 
5.5%
L 26741
 
5.1%
M 26300
 
5.0%
Other values (7) 103705
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 524205
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 72457
13.8%
O 55871
10.7%
N 51014
9.7%
I 42579
8.1%
T 42011
8.0%
A 37772
 
7.2%
D 36986
 
7.1%
C 28769
 
5.5%
L 26741
 
5.1%
M 26300
 
5.0%
Other values (7) 103705
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 524205
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 72457
13.8%
O 55871
10.7%
N 51014
9.7%
I 42579
8.1%
T 42011
8.0%
A 37772
 
7.2%
D 36986
 
7.1%
C 28769
 
5.5%
L 26741
 
5.1%
M 26300
 
5.0%
Other values (7) 103705
19.8%

loan_grade
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size817.5 KiB
A
18936 
B
18373 
C
9877 
D
4196 
E
 
799
Other values (2)
 
137

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters52318
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowC
3rd rowA
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
A 18936
36.2%
B 18373
35.1%
C 9877
18.9%
D 4196
 
8.0%
E 799
 
1.5%
F 109
 
0.2%
G 28
 
0.1%

Length

2025-03-11T13:37:44.141992image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T13:37:44.234927image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
a 18936
36.2%
b 18373
35.1%
c 9877
18.9%
d 4196
 
8.0%
e 799
 
1.5%
f 109
 
0.2%
g 28
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 18936
36.2%
B 18373
35.1%
C 9877
18.9%
D 4196
 
8.0%
E 799
 
1.5%
F 109
 
0.2%
G 28
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52318
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 18936
36.2%
B 18373
35.1%
C 9877
18.9%
D 4196
 
8.0%
E 799
 
1.5%
F 109
 
0.2%
G 28
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52318
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 18936
36.2%
B 18373
35.1%
C 9877
18.9%
D 4196
 
8.0%
E 799
 
1.5%
F 109
 
0.2%
G 28
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52318
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 18936
36.2%
B 18373
35.1%
C 9877
18.9%
D 4196
 
8.0%
E 799
 
1.5%
F 109
 
0.2%
G 28
 
0.1%

loan_amnt
Real number (ℝ)

High correlation 

Distinct499
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9046.9164
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size817.5 KiB
2025-03-11T13:37:44.349571image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q15000
median8000
Q312000
95-th percentile20000
Maximum35000
Range34500
Interquartile range (IQR)7000

Descriptive statistics

Standard deviation5448.1013
Coefficient of variation (CV)0.60220534
Kurtosis1.8019155
Mean9046.9164
Median Absolute Deviation (MAD)3000
Skewness1.2084345
Sum4.7331657 × 108
Variance29681808
MonotonicityNot monotonic
2025-03-11T13:37:44.473037image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 6668
 
12.7%
5000 4723
 
9.0%
6000 4316
 
8.2%
12000 4031
 
7.7%
8000 3090
 
5.9%
15000 3026
 
5.8%
4000 2286
 
4.4%
3000 2085
 
4.0%
7000 1937
 
3.7%
20000 1581
 
3.0%
Other values (489) 18575
35.5%
ValueCountFrequency (%)
500 1
 
< 0.1%
700 1
 
< 0.1%
900 1
 
< 0.1%
1000 361
0.7%
1050 2
 
< 0.1%
1200 152
0.3%
1225 2
 
< 0.1%
1250 2
 
< 0.1%
1275 1
 
< 0.1%
1300 5
 
< 0.1%
ValueCountFrequency (%)
35000 119
0.2%
32000 1
 
< 0.1%
31000 1
 
< 0.1%
30750 1
 
< 0.1%
30000 83
0.2%
29800 2
 
< 0.1%
29100 1
 
< 0.1%
28000 43
 
0.1%
27575 1
 
< 0.1%
27500 1
 
< 0.1%

loan_int_rate
Real number (ℝ)

High correlation 

Distinct351
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.623311
Minimum5.42
Maximum23.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size817.5 KiB
2025-03-11T13:37:44.588031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5.42
5-th percentile6.03
Q17.88
median10.74
Q312.87
95-th percentile15.65
Maximum23.22
Range17.8
Interquartile range (IQR)4.99

Descriptive statistics

Standard deviation3.0031014
Coefficient of variation (CV)0.28268977
Kurtosis-0.72406184
Mean10.623311
Median Absolute Deviation (MAD)2.74
Skewness0.19732536
Sum555790.39
Variance9.018618
MonotonicityNot monotonic
2025-03-11T13:37:44.704897image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.99 1991
 
3.8%
7.51 1976
 
3.8%
7.88 1594
 
3.0%
7.49 1449
 
2.8%
13.49 1293
 
2.5%
11.49 1200
 
2.3%
7.9 1163
 
2.2%
5.42 1019
 
1.9%
11.71 981
 
1.9%
6.03 978
 
1.9%
Other values (341) 38674
73.9%
ValueCountFrequency (%)
5.42 1019
1.9%
5.43 1
 
< 0.1%
5.79 731
1.4%
5.99 504
1.0%
6 4
 
< 0.1%
6.03 978
1.9%
6.05 1
 
< 0.1%
6.17 322
 
0.6%
6.39 97
 
0.2%
6.42 1
 
< 0.1%
ValueCountFrequency (%)
23.22 1
 
< 0.1%
22.11 1
 
< 0.1%
22.06 1
 
< 0.1%
21.74 4
< 0.1%
21.64 1
 
< 0.1%
21.36 6
< 0.1%
21.21 4
< 0.1%
20.89 4
< 0.1%
20.86 2
 
< 0.1%
20.8 1
 
< 0.1%

loan_percent_income
Real number (ℝ)

High correlation 

Distinct60
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15574009
Minimum0
Maximum0.83
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size817.5 KiB
2025-03-11T13:37:44.825472image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04
Q10.09
median0.14
Q30.2
95-th percentile0.33
Maximum0.83
Range0.83
Interquartile range (IQR)0.11

Descriptive statistics

Standard deviation0.089372179
Coefficient of variation (CV)0.57385468
Kurtosis0.70904537
Mean0.15574009
Median Absolute Deviation (MAD)0.06
Skewness0.92871906
Sum8148.01
Variance0.0079873864
MonotonicityNot monotonic
2025-03-11T13:37:45.032868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 3070
 
5.9%
0.08 2674
 
5.1%
0.17 2537
 
4.8%
0.11 2514
 
4.8%
0.13 2463
 
4.7%
0.09 2438
 
4.7%
0.12 2359
 
4.5%
0.07 2347
 
4.5%
0.06 2294
 
4.4%
0.14 2272
 
4.3%
Other values (50) 27350
52.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.01 110
 
0.2%
0.02 407
 
0.8%
0.03 1009
 
1.9%
0.04 1593
3.0%
0.05 2016
3.9%
0.06 2294
4.4%
0.07 2347
4.5%
0.08 2674
5.1%
0.09 2438
4.7%
ValueCountFrequency (%)
0.83 1
 
< 0.1%
0.63 1
 
< 0.1%
0.59 1
 
< 0.1%
0.56 1
 
< 0.1%
0.55 1
 
< 0.1%
0.54 1
 
< 0.1%
0.53 2
 
< 0.1%
0.52 4
 
< 0.1%
0.51 11
 
< 0.1%
0.5 65
0.1%

cb_person_default_on_file
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size459.8 KiB
False
44727 
True
7591 
ValueCountFrequency (%)
False 44727
85.5%
True 7591
 
14.5%
2025-03-11T13:37:45.120286image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

cb_person_cred_hist_length
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8141366
Minimum2
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size817.5 KiB
2025-03-11T13:37:45.198316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median4
Q38
95-th percentile14
Maximum30
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.0249918
Coefficient of variation (CV)0.69227679
Kurtosis3.4727067
Mean5.8141366
Median Absolute Deviation (MAD)2
Skewness1.6160129
Sum304184
Variance16.200559
MonotonicityNot monotonic
2025-03-11T13:37:45.291999image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3 9590
18.3%
2 9455
18.1%
4 9418
18.0%
9 3124
 
6.0%
8 3097
 
5.9%
7 3055
 
5.8%
6 3034
 
5.8%
5 2989
 
5.7%
10 2986
 
5.7%
14 812
 
1.6%
Other values (19) 4758
9.1%
ValueCountFrequency (%)
2 9455
18.1%
3 9590
18.3%
4 9418
18.0%
5 2989
 
5.7%
6 3034
 
5.8%
7 3055
 
5.8%
8 3097
 
5.9%
9 3124
 
6.0%
10 2986
 
5.7%
11 770
 
1.5%
ValueCountFrequency (%)
30 25
< 0.1%
29 21
< 0.1%
28 34
0.1%
27 44
0.1%
26 26
< 0.1%
25 29
0.1%
24 40
0.1%
23 32
0.1%
22 31
0.1%
21 34
0.1%

loan_status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size817.5 KiB
0
45517 
1
6801 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters52318
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 45517
87.0%
1 6801
 
13.0%

Length

2025-03-11T13:37:45.390361image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T13:37:45.467271image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 45517
87.0%
1 6801
 
13.0%

Most occurring characters

ValueCountFrequency (%)
0 45517
87.0%
1 6801
 
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52318
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 45517
87.0%
1 6801
 
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52318
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 45517
87.0%
1 6801
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52318
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 45517
87.0%
1 6801
 
13.0%

age_of_first_emp
Real number (ℝ)

High correlation 

Distinct59
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.853014
Minimum14
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size817.5 KiB
2025-03-11T13:37:45.558246image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile16
Q117
median22
Q326
95-th percentile36
Maximum82
Range68
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.7447909
Coefficient of variation (CV)0.29513791
Kurtosis3.686276
Mean22.853014
Median Absolute Deviation (MAD)4
Skewness1.497953
Sum1195624
Variance45.492205
MonotonicityNot monotonic
2025-03-11T13:37:45.673567image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 11366
21.7%
22 3782
 
7.2%
23 3600
 
6.9%
21 3438
 
6.6%
20 3138
 
6.0%
24 2886
 
5.5%
19 2819
 
5.4%
25 2425
 
4.6%
26 2222
 
4.2%
18 2193
 
4.2%
Other values (49) 14449
27.6%
ValueCountFrequency (%)
14 42
 
0.1%
15 831
 
1.6%
16 11366
21.7%
17 1463
 
2.8%
18 2193
 
4.2%
19 2819
 
5.4%
20 3138
 
6.0%
21 3438
 
6.6%
22 3782
 
7.2%
23 3600
 
6.9%
ValueCountFrequency (%)
82 1
 
< 0.1%
81 1
 
< 0.1%
73 2
 
< 0.1%
70 6
< 0.1%
69 4
< 0.1%
68 2
 
< 0.1%
66 4
< 0.1%
65 3
< 0.1%
64 4
< 0.1%
63 3
< 0.1%

Interactions

2025-03-11T13:37:41.356825image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:33.189841image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:34.772437image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:35.897530image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:37.184287image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:38.038952image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:38.847070image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:39.655490image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:40.434973image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:41.444706image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:33.356481image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:34.870176image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:35.996245image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:37.287088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:38.125673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:38.933910image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:39.740371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:40.521989image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:41.529178image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:33.482675image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:34.955687image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:36.141768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:37.381703image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:38.213487image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:39.022577image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:39.832011image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:40.607802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:41.619444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:33.730911image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:35.053553image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:36.346966image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:37.482308image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:38.310217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:39.115386image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:39.922531image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:40.697739image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:41.711580image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:33.976777image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:35.144737image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:36.495051image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:37.585178image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:38.403854image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:39.211319image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:40.012768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:40.788689image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:41.801819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:34.155485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:35.245366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:36.787045image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:37.680490image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:38.495141image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:39.301941image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:40.099625image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:41.023045image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:41.892033image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:34.322512image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:35.335622image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:36.889499image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:37.774904image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:38.587511image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:39.391868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:40.186828image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:41.112494image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:41.971766image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:34.527770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:35.417250image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:36.981016image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:37.860348image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:38.671336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:39.473608image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:40.266308image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:41.191045image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:42.056530image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:34.665820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:35.805553image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:37.084650image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:37.947073image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:38.756878image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:39.564224image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:40.347965image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:41.272460image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-03-11T13:37:45.760630image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
age_of_first_empcb_person_cred_hist_lengthcb_person_default_on_fileidloan_amntloan_gradeloan_int_rateloan_intentloan_percent_incomeloan_statusperson_ageperson_emp_lengthperson_home_ownershipperson_income
age_of_first_emp1.0000.5670.0470.004-0.0210.0330.0790.0670.0150.0700.669-0.6280.096-0.056
cb_person_cred_hist_length0.5671.0000.0060.0060.0470.012-0.0010.092-0.0290.0260.8050.0340.0430.103
cb_person_default_on_file0.0470.0061.0000.0000.0460.6480.6090.0290.0350.1810.0140.0580.1000.011
id0.0040.0060.0001.0000.0080.0110.0040.0030.0110.0200.0060.0030.000-0.006
loan_amnt-0.0210.0470.0460.0081.0000.0720.0710.0340.7190.1320.0610.0920.0690.368
loan_grade0.0330.0120.6480.0110.0721.0000.7160.0290.0670.4500.0130.0460.1230.009
loan_int_rate0.079-0.0010.6090.0040.0710.7161.0000.0270.1360.399-0.001-0.1140.130-0.087
loan_intent0.0670.0920.0290.0030.0340.0290.0271.0000.0160.0930.0900.0470.0930.002
loan_percent_income0.015-0.0290.0350.0110.7190.0670.1360.0161.0000.419-0.048-0.0620.093-0.327
loan_status0.0700.0260.1810.0200.1320.4500.3990.0930.4191.0000.0230.1140.2380.026
person_age0.6690.8050.0140.0060.0610.013-0.0010.090-0.0480.0231.0000.0610.0430.150
person_emp_length-0.6280.0340.0580.0030.0920.046-0.1140.047-0.0620.1140.0611.0000.1720.222
person_home_ownership0.0960.0430.1000.0000.0690.1230.1300.0930.0930.2380.0430.1721.0000.026
person_income-0.0560.1030.011-0.0060.3680.009-0.0870.002-0.3270.0260.1500.2220.0261.000

Missing values

2025-03-11T13:37:42.185669image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-11T13:37:42.438780image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idperson_ageperson_incomeperson_home_ownershipperson_emp_lengthloan_intentloan_gradeloan_amntloan_int_rateloan_percent_incomecb_person_default_on_filecb_person_cred_hist_lengthloan_statusage_of_first_emp
003735000RENT0.0EDUCATIONB600011.490.17N14037.0
112256000OWN6.0MEDICALC400013.350.07N2016.0
222928800OWN8.0PERSONALA60008.900.21N10021.0
333070000RENT14.0VENTUREB1200011.110.17N5016.0
442260000RENT2.0MEDICALA60006.920.10N3020.0
552745000RENT2.0VENTUREA90008.940.20N5025.0
662545000MORTGAGE9.0EDUCATIONA120006.540.27N3016.0
883769600RENT11.0EDUCATIOND500014.840.07Y11026.0
9935110000MORTGAGE0.0DEBTCONSOLIDATIONC1500012.980.14Y6035.0
11112233000RENT6.0PERSONALB1000011.120.30N2116.0
idperson_ageperson_incomeperson_home_ownershipperson_emp_lengthloan_intentloan_gradeloan_amntloan_int_rateloan_percent_incomecb_person_default_on_filecb_person_cred_hist_lengthloan_statusage_of_first_emp
58634586343085000MORTGAGE6.0PERSONALA50007.510.06N7024.0
58635586353269000RENT0.0DEBTCONSOLIDATIONB1200010.200.17N7132.0
58636586362437000RENT3.0EDUCATIONC900013.490.24Y2021.0
58637586372475000RENT8.0VENTUREB400010.750.05N4016.0
58638586382946610MORTGAGE1.0PERSONALD260017.580.05N6128.0
58639586392270000RENT6.0DEBTCONSOLIDATIONA100007.290.14N4016.0
58641586412828800RENT0.0MEDICALC1000012.730.35N8128.0
58642586422344000RENT7.0EDUCATIOND680016.000.15N2116.0
58643586432230000RENT2.0EDUCATIONA50008.900.17N3020.0
58644586443175000MORTGAGE2.0VENTUREB1500011.110.20N5029.0